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The golden standard for word2vec and topic modeling in general is gensim: https://radimrehurek.com/gensim/models/word2vec.html

I've been using gensim for a few years for topic modeling. It works great with pyLDAvis for visualizations.

How does your implementation differ from the one by Radim?

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It's not a serious implementation but one for didactic purposes:)

I've used gensim's one and it seems great for practical use but I wanted to build an understanding of the underlying algorithm, so built my own from scratch. I was hoping others might learn from it, but it's not really a practical approach to constructing w2v embeddings.

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